The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Recent advancements in the field of video processing have led to development of various algorithms for detection of artefacts in a video. Such techniques for detection of artefacts may be based on a spatial domain analysis or a frequency domain analysis. Further, the artefacts may be attributed to digitisation and/or various types of lossy compression techniques applied on the image for compression, wherein data is quantized and/or discarded. The artefacts may correspond to analog noise, a high frequency noise, ringing, blockiness, and/or the like. The ringing artefacts may result in an appearance of spurious objects near the edges of one or more objects present in the image. The blockiness artefacts may be caused by use of block-based transforms for compression. Such block-based transforms may result in pixilation (macro blocking) in the image when the bit rate is low.
In one of the existing techniques for detection of video noise, one or more speckles that correspond to the image are detected in a radiological image. The noise signal magnitude is determined for the radiological image. Further, the speckle is reported as noise based on the determined noise signal magnitude. In accordance with another technique, a blur in a digital image is determined based on the edges in the image and the spectral energy of the input image. In accordance with another technique, a noise is removed from a Color Filter Array (CFA) based on an application of noise removal techniques classified pixels of an image. The classified pixel may correspond to edge pixels and non-edge pixels. In accordance with another technique, an artefact such as a dead pixel is detected based on a local mean value, local standard difference, a global mean value, a global standard difference are determined for a pixel of interest and a plurality of pixels neighboring the pixel of interest. Further, the aforementioned metric are compared with one or more thresholds to ascertain whether the pixel of interest corresponds to a dead pixel or not.
Evidently, the existing techniques of detection of video artefact detection are either based on a spatial domain analysis or a frequency domain analysis of the image corresponding to a video. Therefore, such techniques are not robust enough as they do not exhaustively explore the combination of spatial as well as temporal artefact detection steps. In order to render high quality media content, it is imperative to minimise the video artefacts. This in turn, requires robust detection of the various types of compression artefacts and their cause, so as to modify the parameters that lead to video artefact minimisation.